nl_causal.base.preprocessing

Module Contents

Functions

unique_columns(X)	Find unique columns for numpy array.
calculate_cor_(X[, thresh, verbose])	Remove low-correlated features.
calculate_vif_(X[, thresh, verbose, method])	Remove multicollinearity features.

nl_causal.base.preprocessing.unique_columns(X)

Find unique columns for numpy array.

Parameters:

X: {array-like, sparse matrix} of shape (n_samples, n_features)

Feature matrix

Returns:

index: The index set for unique subset

nl_causal.base.preprocessing.calculate_cor_(X, thresh=0.8, verbose=0)

Remove low-correlated features.

Parameters:

X: {array-like, sparse matrix} of shape (n_samples, n_features)

Feature matrix

Returns:

X: return feature matrix by removing low-correlated features.

nl_causal.base.preprocessing.calculate_vif_(X, thresh=2.5, verbose=0, method='best')

Remove multicollinearity features.

Parameters:

X: {array-like, sparse matrix} of shape (n_samples, n_features)

Feature matrix

Returns:

X: return feature matrix by removing multicollinearity features.